01
A model wired to a real decision.
We sharpen the question before we pick the model, so the answer lands on the decision you're actually trying to make, not the analysis your team thought to run.
Data-driven.
Outcome-oriented.
We don't just stand up dashboards, models and pipelines. We plan, build and run the data, analytics and AI systems that drive your most important business outcomes.
About
The partner behind growth-stage performance.
We plan what's worth building, build the infrastructure, dashboards, models and agents that deliver it, and stay on to keep the platform driving performance as your business evolves.
If your business has outgrown spreadsheets and off-the-shelf tools, you need a partner working alongside you, not under you, on the performance of your data, analytics and AI.
Trusted by
Services
Six disciplines. One performance-driven analytics and AI partner.
01
Measurement &
Attribution
Tracking, tagging and attribution that unify every channel into one source of truth, so marketing investment is measured against revenue, not vanity metrics.
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02
Data
Engineering
Warehouses, pipelines and transformation layers built to scale with your growth and survive the next inflection.
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03
Data Governance
& MDM
Governance, quality and master data, so finance, ops and revenue teams act on numbers they trust.
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04
Business
Intelligence
Self-serve BI with a governed semantic layer, so leadership runs on one set of numbers and decisions move faster.
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05
Analytics &
Reporting
Dashboards, reporting and experimentation built around the decisions you and your team make every week.
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06
AI & Machine
Learning
Machine learning models and AI agents grounded in a data foundation built to support them, and to scale as adoption grows.
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Approach
What it takes to move a business metric.
02
A stack your team can keep running.
We ship systems your engineers can run the day after we leave. No boutique tooling that needs us back every quarter, no scope that outruns your data foundation. The result: a platform that keeps producing value long after the engagement closes.
03
A business metric that actually moves.
Every engagement closes on a result the executive team can point to: a forecast that holds up to scrutiny, a churn curve that flattens, a sales motion that runs against a ranked list, a metric finance trusts. Pretty dashboards nobody opens are not the goal.
04
AI that survives the year after launch.
If a use case won't survive a year of real data and real users, we say so. We've turned down agent projects where the data foundation wasn't ready, and we'd rather say no than ship a demo that quietly breaks.
Engagement models built around your stage
Blueprint
2-4 weeks
Assessment, opportunity sizing and a prioritized roadmap. Each priority anchored to a target business metric, so the next engagement runs against a number, not a deliverable. The deliverable is the plan.
Build
2-24 weeks
Foundation, dashboards and models shipped to production. Documentation, handoff and runbooks included.
Run
ongoing
Managed analytics, model operations and quarterly platform reviews. The platform evolves as your business changes.
Embedded
ongoing
An analytics or data engineer integrated into your team for as long as you need them. For when capacity is the constraint, not strategy.
Fractional
ongoing
A partner-level data leader running your analytics or AI function. Strategy, hiring plan, vendor decisions and reporting up to the executive team or board. For mid-market teams without a CDO/CAO seat.
Partners
Built with the stack. Embedded with the network.
Technology partners
Capital partners
Selected Work
What the outcomes look like.
01
National ecommerce grocery · market expansion
Real-time ad performance by market and channel, so the marketing team optimized spend on launch day, not in the next monthly report.
Read case study
02
Regional QSR · multi-unit
Unified POS, payroll, loyalty and financial data into Tableau dashboards operators actually opened and used to run a shift.
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03
DTC lifestyle apparel · growth stage
Consolidated Shopify, inventory and customer data into one platform, so merchandising, supply and CX teams worked from the same numbers, not three.
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04
B2B SaaS · growth stage
Churn model flagged at-risk accounts six months early and lifted retention by 15% on that cohort.
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Field Notes
What we're seeing in the field.
Your AI strategy is fragmenting along department lines.
When every function buys their own AI tools, you get duplicated knowledge, conflicting models and no compounding learning. The org-design fix matters more than the tooling choice.
Read
Your dashboard is lying to you.
Your three teams are looking at three different revenue numbers and nobody is sure which is right. A contrarian take on what 'data-driven' actually requires.
Read
Notes from a two-week Blueprint with a DTC brand.
What we looked at, what we found and the three recommendations that shaped what got built first.
Read
Why marketing is reconsidering autonomous AI agents.
Marketing was supposed to be agentic-first. A practical look at what actually shipped, what didn't and why the autonomous-agent pitch is getting a more careful reception.
Read
Outcomes start with a Blueprint. We plan, build and run from there.
Thirty minutes with a 829 Analytics partner. You leave with a prioritized view of what to build first, what's worth waiting on, and the business metric anchoring each move. Whether or not we end up working together.